Portrait of Šimon Hudínek

Šimon Hudínek

Model it. Control it. Wire it. Make it run. Control and field engineering, end to end.

Discipline

Control & Field Systems Engineer

Status

Open for Field & Control Roles

Origin

Czech Republic

Projects

Škoda Auto CVS Humidity Simulation Tool

Test & Simulation Engineer

One engineer, the whole loop: problem framing, measurement design, sensor instrumentation, a validated model, and the operator app that runs it — 1st place at SVK 2025.

Internship Report (signed) ↓

CVS emission benches are humidity-sensitive: condensation in the dilution tunnel invalidates a test run. I owned the loop end to end — framed the problem, designed the measurement campaign, instrumented the test cell, built the thermodynamic model, validated it against PEMS data, and shipped the operator GUI that flags condensation-prone runs before they start. The panel is that pre-check tool rebuilt in the browser.

Test Conditions
Condensation Check
Dewpoint
0.0 °C
Wall
0.0 °C
VALID
Ambient hum. ratio
Dilute-mix H₂O
Dilute-mix dewpoint
Dewpoint margin
Dewpoint Sweep — Ambient RH 0–100 %
Dilute-mix dewpointTunnel wall / filter tempOperating point

Model-Based SCR Dosing & AdBlue Injector Control

Master's Thesis — Control Engineer

The difficult kind of control problem: stiff nonlinear catalyst chemistry, conflicting objectives, and no hardware safety net.

Too little AdBlue and NOx escapes; too much and ammonia slips through — which is why production exhaust systems carry an extra ammonia-slip catalyst (ASC) behind the SCR. My thesis NMPC previews load steps 8 minutes ahead and holds NH3 slip below 1 ppm, so the ASC can be deleted — traded against some NOx conversion at partial load. With no hardware to fall back on, every claim had to survive validation against the XMR monolith reactor simulator. The panel shows the system and the benchmark.

EXHAUSTTAILPIPEDIESELGENERATORLOAD 10–100 %TT150–427 °CQTNOx≤ 2734 ppmADBLUETANKSCR CATALYSTFe-ZSM5 monolithQTNOxconv 92.3 %QTNH3≤ 0.9 ppmASCwith PID: 262 ppm slip → mandatoryNOT REQUIRED — slip < 1 ppmNMPC DOSING CONTROLLERinternal 1D SCR model · prediction horizon 8 minu(t) urea
EXHAUST AFTERTREATMENT TREND — 420 MIN LOAD CYCLE (XMR BENCHMARK)PIDNMPC
10 %25 %50 %75 %100 %75 %STOPLOAD10090807060engine off10001001010.10.0110 ppm slip target1NOx CONVERSION [%]NH3 SLIP [ppm] · LOGPID: 262 ppmNMPC ≤ 0.9 ppm across the cycle2060120180240300360420 min

1 The NMPC previews 8 min ahead: the load step at t = 190 min is already inside its horizon, so dosing tapers before it — no slip, no ASC. The PID reacts late: 262 ppm.

2 The price: lower NOx conversion at low load — still within emission limits — in exchange for one catalyst less and less urea (92.3 % vs 95.0 % over the cycle, −6 % urea).

Multi-Platform Water Control System

CTO / Lead Control Engineer

Leading an 8-engineer team from sensor wiring to operator GUI — nonlinear MPC delivered on a real hydraulic plant, on schedule.

As CTO of an 8-engineer semester team I owned the architecture, split the hardware and software branches, and integrated them into one system delivered on schedule. The stack: first-principles model, nonlinear MPC with EKF state estimation, NI-DAQmx acquisition, and a PySide6 operator app — stable closed-loop control on the real rig. The panel is that operator app, replaying a closed-loop run at 6× real time.

Control Panel
Clock00:00:00
Upper Tank
0.0 cm
Lower Tank
0.0 cm
Main Panel — Trend
Lower Tank (H2, measured)Target Level (SP)Control Action (U)

Embedded & Smart Systems

Personal Projects — Embedded Developer

Circadian lighting cabinet with local server control, an ESP32 smart bottle, and a LabVIEW test-rig restoration.

Off the clock I build the things I use: an ESP32 circadian lighting cabinet driven by a local server (no cloud, running as a daily-driver), a capacitive-sensing smart bottle that logs hydration patterns, and a laboratory test rig brought back from a hardware failure by remapping its wiring and rebuilding the LabVIEW control logic. Click a photo below for the full story of each.

Field Log

Lab, Plant & Rock

01 / 07

Walking the committee through the multi-tank water plant on delivery day — nonlinear MPC running live on the laptop beside the rig.

Stack

Experience & Tools

Timeline · hover the markers

SVK ’22

SVK ’23 · 2nd

SVK ’24

AI Hack · 4th

SVK ’25 · 1st

2021

2022

2024

03–08/25

09–12/25

2026

Foundations & Tools

The foundation is physics, chemistry, and control theory from UCT Prague — sensors, process modeling, and signal processing in depth. Tools are picked per project and learned as needed, with AI in the loop.

Foundations — UCT Prague

  • Modeling of chemical & physical processes
  • Sensor principles & measurement theory
  • Signal processing & system identification
  • Control theory — MPC · state-space · PID
  • Thermodynamics & fluid dynamics

Tools Used in Projects

  • Python — NumPy · SciPy · CasADi · PySide6
  • MATLAB / Simulink
  • NI-DAQmx · LabVIEW
  • ESP32 · C/C++
  • Linux · Git

How I Work

  • First-principles model before the controller
  • Every model validated against measured data
  • New tools picked up per project, AI-assisted
  • Hands on the hardware, not just the simulation

Contact

About & Contact

Ing. in Sensorics and Cybernetics in Chemistry. Control and field engineering is where I want to be: deriving the model, writing the MPC, then wiring the sensors and climbing the rig to make it run. Equally at home in Simulink and on a ladder.